D7.9 Administration and User Manuals for SimICI System final

Transcription

1 Contractual Delivery Date: 04/2018 Actual Delivery Date: 10/2018 Type: Report Version: v1.0 Dissemination Level [Public] Deliverable Statement This deliverable describes the administrative and user manual for the SimICI system. It presents the way that CIRP and flooding models should be operated by the users. Copyright by the EU-CIRCLE consortium, EU-CIRCLE is a project that has received funding from the European Union s Horizon 2020 research and innovation programme under grant agreement No Please see more information. DISCLAIMER: This document contains material, which is the copyright of EU-CIRCLE consortium members and the European Commission, and may not be reproduced or copied without permission, except as mandated by the European Commission Grant Agreement no for reviewing and dissemination purposes. The information contained in this document is provided by the copyright holders "as is" and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall the members of the EU-CIRCLE collaboration, including the copyright holders, or the European Commission be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of the information contained in this document, even if advised of the possibility of such damage.

2 Preparation Slip Name Partner Date From A.S. Chen / A. Kostaridis UNEXE/STWS 10/2018 Reviewer Reviewer For delivery A. Sfetsos NCSRD 09/2018 Document Log Issue Date Comment Author / Organization V0.1 08/2018 ToC UNEXE V0.2 10/2018 CIRP Admin and User Manual for SIMICI STWS List here the changes and their rational for each release Grand Agreement [Public] Page 1

3 Executive Summary The EU-CIRCLE climate hazard mapping and impact assessment are integrated in the Critical Infrastructure Resilience Platform (CIRP) for end-users applications and outcome visualisation. Deliverable D7.9 summarises the integration and demonstrates the functions of CIRP via the Virtual Data Set described in D7.4. Grand Agreement [Public] Page 2

4 Contents EXECUTIVE SUMMARY... 2 CONTENTS INTRODUCTION EU-CIRCLE CRITICAL INFRASTRUCTURE RESILIENCE PLATFORM Environment Setup CIRP Administration... 7 The System Administration View CIRP User manual Climate Data Analyses Ingesting RCP Datasets Ingesting Forest Fire Indices Datasets Creating a New Scenario Selection and Execution of Analyses CADDIES 2D USER MANUAL Introduction CAFLOOD EXECUTABLE Execution and configuration file examples SUMMARY REFERENCES Grand Agreement [Public] Page 3

5 1 Introduction Using CIRP, the users can select the scenarios and dataset for carrying out hazard modelling and data analyses via the built-in map. The user friendly interface allows users to browse and define the study areas, amending the input data to execute various analyses. The high performance simulators such as CADDIES 2D for flood simulation are embedded in CIRP. Users can easily prepare the input data to execute simulations, and visualise modelling results. The outcomes can be further utilised by other data processing modules to provide in-depth analyses. For example, the hazard information can be combined with fragility/vulnerability functions to determine the risk of critical infrastructure to climate disasters. Grand Agreement [Public] Page 4

6 2 EU-CIRCLE Critical Infrastructure Resilience Platform D7.9 Administration and User Manuals for SimICI System final Within EU-CIRCLE, the Critical Infrastructure Resilience Platform (CIRP) was developed to integrate various climate hazard modelling and impact assessment methodologies to demonstrate and visualise the analysing results to the stakeholders. The following section describes the environment setup, section 2.2 describes the administration framework and section 2.3 presents the user guide (the execution of a selected set of CIRP analyses using the SIMICI dataset of the Virtual City). 2.1 Environment Setup CIRP is a multi-user enterprise software accessible over the Web as a Rich Client software application (Web Start Technology). The following prerequisites are required: Windows OS is only supported Java Runtime 1.8.0_121 (32 or 64 bit) or later An R Interpreter Configuration Steps: 1. Install the Java Runtime environment (if not exist in your system) 2. Open the Windows Control Panel and click the Java icon 3. The Java Control Panel Window will popup. Click the Security Tab and press the Edit Site List button. 4. Add the following entry: (see screenshot below) and press OK Grand Agreement [Public] Page 5

7 5. Finally make sure that the following ports are not blocked by your organization s network: 1098, 1099, 3873, 4447, 8080 and Navigate to the URL and download the latest R version 7. Installation path is: C:\Program Files\R\R-3.X.X (depending on the version) 8. Open your Windows Explorer and navigate to the installation path 9. Run the R.exe located in bin folder of R installation as Administrator and type the following command: install.packages( extremes ) 10. Once the prerequisites are installed you can visit the following webpage: Press the Launch button 12. A file named cirp.jnlp will be downloaded. Double click it. 13. The very first time the CIRP application will start the download process. A similar window will appear with a progress bar indicating the download progress. 14. Please note that the initial application download takes place only once Grand Agreement [Public] Page 6

8 15. Once download is completed the Security Warning window will appear. 16. Press I accept the risk and want to run this application 17. After a while login screen will be shown 18. Fill in the user credentials provided by the Administrator (STWS) and press Connect. 19. As a final step and once the GUI is shown press the Menu Preferences 20. In the pop window (see below) press the R interpreter option on the left side 21. Enter C:/Program Files/R/R3.X.X as the R installation path and press OK. 2.2 CIRP Administration In CIRP each user may possess one or more Roles. A CIRP role is associated with a set of Views and tools. Grand Agreement [Public] Page 7

9 In general the CIRP User Interface is divided into a set of Perspectives. A Perspective is a set of Views and tools organized into dockable windows. The administrator Role is a pre-defined role that is able to access the Administrator perspective. The latter is accessible via the main toolbar (icon on the left of Figure 1). Figure 1: Toolbar for perspective selection The administrator perspective provides the CIRP administration tools for User, Role and Access Rights management. Users are organized into organizations. It consists of three main Views: System administration View Users View The System Administration View Using the options under the User and Roles Management section, the administrator is able to create edit and delete users and user roles. Figure 2: Managing users and roles For a new user, the administrator must fill in the following information: Name Surname Username: Password Department Etc. The Create button creates the new user. All the compulsory fields are marked with a special icon to help the administrator recognize what information is missing in order to be allowed to continue to the next page of the wizard. Grand Agreement [Public] Page 8

10 . Figure 3: Create New User Figure 4: Assign User Roles Figure 5: Assign Access Rights Regarding User Roles the system gives the ability to manage any number of Roles by providing access to the system Perspective, Views and toolbars. A user can be assigned more than one Role. A user who has the predefined role of Administrator can perform the following functions: Create or Delete an Organization s Departments Grand Agreement [Public] Page 9

11 Create or Delete Users Create or Delete Roles Parametrize the System (Application Settings) The Administrator can perform organizational management and tune the system according to the requirements of the users and the organization. 2.3 CIRP User manual The graphical user interface of CIRP consists of different perspectives accessible from buttons of the main toolbar and each perspectives consists of a set of Views (dockable windows). The following Figure presents the main perspective of CIRP where impact assessment scenarios can be executed. The Catalog View allows the management of repositories (Local or Public) and their datasets. The following dataset types are currently supported: Feature Datasets (shapefiles) Grand Agreement [Public] Page 10

12 Raster Datasets (Ascii Grid) Fragility/Damage Curves (XML) Fragility/Damage Curves Mappings (XML) Tables (CSV Files) Grid Datasets (NetCDF Files) The analysis toolbox provides the following categories: Climate Data Building Decision Support GIS Hazard Lifeline Climate Data Analyses A Climate data analysis uses CSV datasets as input and performs Extreme Value Theory and Frequency Analysis. The following analysis are currently provided in the Climate Data Analysis category. Temperature Extremes and Return Period Analysis Wind Speed Extremes and Return Period Analysis Precipitation Extremes and Return Period Analysis Drought Code Index Extremes and Return Period Analysis Fine Fuel Moisture Code Index Extremes and Return Period Analysis Forest Fire Weather Index Extremes and Return Period Analysis Fire Weather Index Analysis Probability of Exceedance Analysis Grand Agreement [Public] Page 11

13 An input dataset file for the Virtual City has the following filename convention: And sample content: Where: T: Temperature WS: Wind Speed R_D: Rain Daily RH_D: Relative Humidity Daily SM_D: Soil Moisture Daily Ingesting RCP Datasets Follow the steps below: 1. Right click the local repository and select the menu option Ingest 2. The Ingestion Wizard will popup 3. Select the Table file type Grand Agreement [Public] Page 12

14 4. Select the RCP CSV file from our local disk and press the Next button 5. In the Select Data type wizard page select the Representative Concentration Pathways from the drop down and press the Next button. 6. In the Set attribute mappings page if the ingested CSV file has already the correct column names, CIRP will read them and the mapping is automatic. In this case you can press the Next button. Grand Agreement [Public] Page 13

15 7. The Set extra field information wizard page is empty so you can press immediately the Next button 8. Enter a descriptive name for this dataset and a version number. Then press the Finish button. 9. Your ingested dataset will appear in the Climate Data -> Representative Concentration pathways folder in your Local Cache. Another method is to ingest multiple RCP Cordex file in one shot Grand Agreement [Public] Page 14

16 Right click the local repository and select the menu option Ingest The Ingestion Wizard will popup Select the MultiTable file type Follow the previous steps without specifying a name for the ingested file Ingesting Forest Fire Indices Datasets Forest Fire Indices are CSV datasets that can be ingested in the same way as RCP datasets (one file or multiple files). The difference is the data type wizard page (see following Figure) where you need to choose Forest Fire Indices). The Forest Fire Indices datasets have the following naming convention and columns: DC_D: Drought Code Daily FMC_D: Fine Fuel Moisture Code Daily FWI_D: Fire Weather Inded Daily Creating a New Scenario In order to Create a new Scenario press the New Scenario button in the Scenario Manager View toolbar Grand Agreement [Public] Page 15

17 The Create Scenario wizard will appear. Enter a name for your scenario and optionally enter a description. Untick the Use Region of Interest checkbox and press the Next button. In the next page select CIRP Default Set and press Finish. The scenario appears in the Scenario Manager View Selection and Execution of Analyses Precipitation Extreme Values and Return Periods Analysis In order to execute the Precipitation Extreme Values and Return period analysis, Press the Execute Analysis button from the Scenario Manager view Grand Agreement [Public] Page 16

18 Unfold the Climate Data category, select the Precipitation Extreme Value and Return Period Analysis option and press Finish. The analysis graph View will be opened. Click on the blue box Representative Concentration Pathways in order to select an RCP dataset. Press the Search for a dataset option and select an ingested dataset from the popup window. You can filter the dataset names by typing in the text box. Then press Finish. Grand Agreement [Public] Page 17

19 The blue box will become green as the input dataset has been chosen. Click the red analysis box to set the rest of the analysis options. Select a name for the output file Grand Agreement [Public] Page 18

20 Select the distribution type and alpha value Select the return periods for which you wish to calculate the extreme values Once all parameters are set the red box becomes red and the execute button is activated Press the Execute button After some seconds the analysis execution will be completed. A new file will be created in the Scenario tree and appears on the map with the attributes depending on the return periods selected. The default layer style is Ranged based on prec100 column data Change the resulted datasets (feature layer) style in order to visualize any return period on the map Select both the Field Classified to be the same with the Label (e.g. prec500) Grand Agreement [Public] Page 19

21 Temperature Extreme Values and Return Periods Analysis Select from the Climate Data category the Temperature Extreme Value and Return Period Analysis option and press Finish. Follow the steps described in the Precipitation Extreme Values and Return Periods Analysis in order to load the RCP datasets for the Virtual City In the Analysis View: Select a name for the output file Select either Max, Min or Median for the temperature values Select the return periods Optionally select the distribution type and alpha value Once all parameters are set the red box becomes red and the execute button is activated Press the Execute button Grand Agreement [Public] Page 20

22 After some seconds the analysis execution will be completed. A new file will be created in the Scenario tree and appears on the map with the attributes depending on the return periods selected. The default layer style is Ranged based on temp100 column data You can change the resulted datasets (feature layer) style in order to visualize any return period on the map Select both the Field Classified to be the same with the Label (e.g. temp500) Grand Agreement [Public] Page 21

23 Wind Speed Extreme Values and Return Periods Analysis D7.9 Administration and User Manuals for SimICI System final Select from the Climate Data category the Wind Speed Extreme Value and Return Period Analysis option and press Finish. Follow the steps described in the Precipitation Extreme Values and Return Periods Analysis in order to load the RCP datasets for the Virtual City In the Analysis View: Select a name for the output file Select the return periods Optionally select the distribution type and alpha value Once all parameters are set the red box becomes red and the execute button is activated Press the Execute button After some seconds the analysis execution will be completed. A new file will be created in the Scenario tree and appears on the map with the attributes depending on the return periods selected. Forest Fire Weather Index (FWI) Extreme Values and Return Periods Analysis This analysis takes as input Forest Fire Indice grid files (CSVs) and calculates the Fire Weather Index Extreme values per return period. The output of the analysis is a POINT feature layer (shapefile) with feature per input grid location. For each feature the FWI value and associated class for each return period is calculated. Thus the following attributes are produced: fwi10 (10 years FWI value) fwi20 (20 years FWI value) fwi25 (25 years FWI value) fwi50 (50 years FWI value) fwi100 (100 years FWI value) fwi500 (500 years FWI value) fwi10cl (10 years class) fwi20cl (20 years class) fwi25cl (25 years class) fwi50cl (50 years class) fwi100cl (100 years class) fwi500cl (500 years class Press the Execute Analysis button to select an analysis Grand Agreement [Public] Page 22

24 Unfold the Climate Data category, select the Forest Fire Weather Index Extreme Value and Return Period Analysis option and press Finish. Click on the blue box Forest Fire Indices in order to select a dataset. Press the Search for a dataset option and select an ingested dataset from the popup window. You can filter the dataset names by typing in the text box. Then press Finish. The blue box will become green as the input dataset has been chosen. Click the red analysis box to set the rest of the analysis options. Grand Agreement [Public] Page 23

25 Select a name for the output file Select the return periods Optionally select the distribution type and alpha value Once all parameters are set the red box becomes red and the execute button is activated Press the Execute button After some seconds the analysis execution will be completed. A new file will be created in the Scenario tree and appears on the map with the attributes depending on the return periods selected. The default layer style is Ranged based on fwi100cl column data You can change the resulted datasets (feature layer) style in order to visualize any return period on the map E.g. select the Field Classified to be fwi500cl and the same the Label (e.g. fwi500) Grand Agreement [Public] Page 24

26 Drought Code Extreme Values and Return Periods Analysis This analysis takes as input Forest Fire Indice grid files (CSVs) and calculates the Drought Code Extreme values per return period. The output of the analysis is a POINT feature layer (shapefile) with feature per input grid location. For each feature the drought code value and associated class for each return period is calculated. Thus the following attributes are produced: dc10 (10 years drought code value) dc20 (20 years drought code value) dc25 (25 years drought code value) dc50 (50 years drought code value) dc100 (100 years drought code value) dc500 (500 years drought code value) dc10cl (10 years class) dc20cl (20 years class) dc25cl (25 years class) dc50cl (50 years class) dc100cl (100 years class) dc500cl (500 years class) Press the Execute Analysis button to select an analysis Unfold the Climate Data category, select the Drought Code Index Extreme Value and Return Period Analysis option and press Finish. The analysis graph View will be opened Click on the blue box Forest Fire Indices in order to select a dataset. Press the Search for a dataset option and select an ingested dataset from the popup window. Grand Agreement [Public] Page 25

27 You can filter the dataset names by typing in the text box. Then press Finish. The blue box will become green as the input dataset has been chosen. Click the red analysis box to set the rest of the analysis options. Select a name for the output file Select the return periods Optionally select the distribution type and alpha value Once all parameters are set the red box becomes red and the execute button is activated Press the Execute button After some seconds the analysis execution will be completed. A new file will be created in the Scenario tree and appears on the map with the attributes depending on the return periods selected. The default layer style is Ranged based on dc100cl column data Grand Agreement [Public] Page 26

28 You can change the resulted datasets (feature layer) style in order to visualize any return period on the map E.g. select the Field Classified to be dc500cl and the same the Label (e.g. dc500) Fine Fuel Moisture Code (FFMC) Extreme Values and Return Periods Analysis This analysis takes as input Forest Fire Indice grid files (CSVs) and calculates the Fine Fuel Moisture Code Extreme values per return period. The output of the analysis is a POINT feature layer (shapefile) with feature per input grid location. For each feature the drought code value and associated class for each return period is calculated. Thus the following attributes are produced: ffmc10 (10 years fine fuel moisture code value) ffmc20 (20 years fine fuel moisture code value) ffmc25 (25 years fine fuel moisture code value) ffmc50 (50 years fine fuel moisture code value) ffmc100 (100 years fine fuel moisture code value) ffmc500 (500 years fine fuel moisture code value) ffmc10cl (10 years class) ffmc20cl (20 years class) ffmc25cl (25 years class) ffmc50cl (50 years class) ffmc100cl (100 years class) ffmc500cl (500 years class) Press the Execute Analysis button to select an analysis Unfold the Climate Data category, select the Fine Fuel Moisture Code Index Extreme Value and Return Period Analysis option and press Finish. The analysis graph View will be opened Click on the blue box Forest Fire Indices in order to select a dataset. Press the Search for a dataset option and select an ingested dataset from the popup window. You can filter the dataset names by typing in the text box. Then press Finish. The blue box will become green as the input dataset has been chosen. Click the red analysis box to set the rest of the analysis options. Select a name for the output file Select the return periods Optionally select the distribution type and alpha value Once all parameters are set the red box becomes red and the execute button is activated Press the Execute button After some seconds the analysis execution will be completed. Grand Agreement [Public] Page 27

29 A new file will be created in the Scenario tree and appears on the map with the attributes depending on the return periods selected. The default layer style is Ranged based on ffmc100cl column data You can change the resulted datasets (feature layer) style in order to visualize any return period on the map E.g. select the Field Classified to be ffmc500cl and the same the Label (e.g. ffmc500) Forest Fire Indices Threshold Statistical Analysis This analysis takes as input Forest Fire Indice grid files (CSVs) and calculates the number of days a Forest Fire Index exceeds or not a threshold value The output of the analysis is a POINT feature layer (shapefile) with feature per input grid location. For each feature the following attributes are calculated: ffi_var (forest fire index variable name. can take values: dc_c, ffmc_d, fwi_d) ffmc20 (20 years fine fuel moisture code value) operator (denotes the comparison against the threshold value) threshold (the threshold value) startdate (starting date of input FFI data) enddate (ending date of input FFI data) no_days (number of days) Press the Execute Analysis button to select an analysis Unfold the Climate Data category, select the Forest Fire Indices Threshold Statistics Analysis option and press Finish. Click on the blue box Forest Fire Indices in order to select a dataset. Press the Search for a dataset option and select an ingested dataset from the popup window. You can filter the dataset names by typing in the text box. Then press Finish. Click the red analysis box to set the rest of the analysis options. Select a name for the output file Grand Agreement [Public] Page 28

30 Select the FFI index Once all parameters are set the red box becomes red and the execute button is activated Press the Execute button After some seconds the analysis execution will be completed. A new file will be created in the Scenario tree and appears on the map with the attributes depending on the return periods selected. Probability of Exceedance Analysis This analysis calculates probability of exceedance for any given climate variable based on the value set via the Analysis UI. You can also select the probability distribution and set an alpha value. The resulting probability is shown via a popup dialog after the analysis is finished. Fire Weather Index Analysis This analysis calculates the Calculates the Canadian Fire Weather Index (FWI) from mulitple cordex datasets Assuming that cordex files have already been ingested Select the new analysis from the Climate Data category Grand Agreement [Public] Page 29

31 Select one or more cordex datasets from the Repository For each of the selected cordex datasets a new file will be created that contains the FWI for Date Press the Execute Button The resulting files have the same name as the cordex files with the additional suffix _FWI ) Transmission Tower Fire Physical Damage This analysis determines if steel transmission pylons are physically damaged due to fire based on the following steps: get fireline intensity per computational cell, estimate temperature, find strength of structure, select exposure, determine fire duration, and finally find damage value from fragility curve. The result value is added to the column damagefactor of Asset attribute table. Assuming that new scenario with unticked Use Region of Interest checkbox was created. Grand Agreement [Public] Page 30

32 Unfold the Lifeline category and select the Transmission Tower Fire Physical Damage option and press Finish. The analysis graph View will be opened. Click on the blue boxes Transmission Towers (Assets), Fireline Intensity (kw/m) and Rate of Spread (m/min) in order to select an RCP datasets. Press the Search for a dataset option and select an ingested dataset from the popup window. Then press Finish. The blue box will become green as the input dataset has been chosen Select a name for the output file Select the Ambient Temperature, Height above ground, and Replacement cost per pylon Press the Execute button After some seconds the analysis execution will be completed. A new file will be created in the Scenario tree under the folder name Transmission Tower Fire Physical Damage Double click the file to view its contents Grand Agreement [Public] Page 31

33 Asset Fire Damage Analysis This analyses intersects fire contours given as user input with assets given as user input. All assets that intersect at least one fire contour are assumed to be completely damaged (damage = 1). All other assets are assumed not be damaged at all (damage = 0). Assuming that new scenario with unticked Use Region of Interest checkbox was created. Unfold the Lifeline category and select the Asset Fire Damage option and press Finish. Grand Agreement [Public] Page 32

34 The analysis graph View will be opened. Click on the blue boxes Assets and Fire Contours in order to select an RCP datasets. The blue box will become green as the input dataset has been chosen. Select a name for the output file Press the Execute button After some seconds the analysis execution will be completed. A new file will be created in the Scenario tree under the folder name Asset Fire Damage Double click the file to view its contents Grand Agreement [Public] Page 33

35 Electric substations cut from electricity supply analysis This analysis takes power lines, electricity stations and fire contour datasets a user input. The power line data sets needs to have topological information, i.e. the ids of the electricity stations that are connected by this power line (including electricity flow direction indicated by source and target id). Power lines can be both types, aboveground/aerial or buried. It is analysed, if electricity station is connected to the electricity network by power line that is burned during fire incident or that is connected to another electricity substation that is connected to the electricity network by power line that is burned then remaining capacity of current substation is reduced to the value proportional to the whole network capacity loss value and the number of non served clients will consequently increase. Assuming that new scenario with unticked Use Region of Interest checkbox was created. Unfold the Lifeline category and select the Electric substations cut from electricity supply analysis option and press Finish. Grand Agreement [Public] Page 34

36 The analysis graph View will be opened. Click on the blue boxes Power Lines with Topology Information, Electricity Substations with ID and Number of Clients and Fire Contours in order to select an RCP datasets. Press the Search for a dataset option and select an ingested dataset from the popup window. Then press Finish. The blue box will become green as the input dataset has been chosen. Select a name for the output file Press the Execute button After some seconds the analysis execution will be completed. A new file will be created in the Scenario tree under the folder name Electric substations cut from electricity supply analysis. Double click the file to view its contents Grand Agreement [Public] Page 35

37 Estimated Number and Replacement Cost of wooden pylons Analysis This analysis calculates the replacement costs of wooden pylons that have been burned during a fire incident. For this purpose, it is assumed that the average distance between two adjacent pylons is known by the user. In the analysis it is tested, if a given fire contour intersects with an individual powerline segment. If there is an intersection, it is assumed that all pylons of this segments need complete replacement. The number of pylons per segment is estimated by dividing the length of the segment of the average distance of two adjacent pylons. All other pylons do not have associated replacement costs. The replacement costs per pylon have to be entered as a user Input. Assuming that new scenario with unticked Use Region of Interest checkbox was created. Unfold the Lifeline category and select the Replacement costs of burned wooden pylons based on estimation of individual pylons option and press Finish. Grand Agreement [Public] Page 36

38 The analysis graph View will be opened. Click on the blue boxes Power Lines (without Topology Information) and Fire Contours in order to select an RCP datasets Press the Search for a dataset option and select an ingested dataset from the popup window. Then press Finish. The blue box will become green as the input dataset has been chosen. Select a name for the output file Select the Average distance between pylons and the Cost per wooden pylon Press the Execute button After some seconds the analysis execution will be completed. A new file will be created in the Scenario tree under the folder name Replacement cost of burned wooden pylons. Double click the file to view its contents Grand Agreement [Public] Page 37

39 Replacement cost of wooden pylons Analysis This analysis calculates the replacement costs of wooden pylons that have been burned during a fire incident. For this purpose, it is assumed that the exact location of each individual wooden pylon is known. In the analysis it is tested, if a given fire contour intersects with each individual pylon. If there is an intersection, it is assumed the pylon needs complete replacement. All other pylons do not have associated replacement costs. The replacement costs per pylon have to be entered as a user Input Assuming that new scenario with unticked Use Region of Interest checkbox was created. Unfold the Lifeline category and select the Replacement costs of burned wooden pylons based on individual pylon dataset option and press Finish. Grand Agreement [Public] Page 38

40 The analysis graph View will be opened. Click on the blue boxes Assets and Fire Contours in order to select an RCP datasets. Press the Search for a dataset option and select an ingested dataset from the popup window. Then press Finish. The blue box will become green as the input dataset has been chosen. Select a name for the output file Select the Cost per wooden pylon Press the Execute button After some seconds the analysis execution will be completed. A new file will be created in the Scenario tree under the folder name Replacement cost of burned wooden pylons based on indiv pylon dataset. Double click the file to view its contents Grand Agreement [Public] Page 39

41 Clients not served with Electricity Analysis This analysis is an economic impact analysis. It analyses the loss of revenue for an electricity operator due to non serving energy to customers. For this purpose the loss of not served energy to customers due to damaged electricity stations is computed. The number of clients connected to each electricity station and the state of each electricity station (damage yes/no?) need to be known as an input. The power station out of work time, energy cost per hour and average power consumption per client are parameters that have to be entered by the user. The analysis calculates the energy losses due to power station not working time by multiplying the total number of clients retrieved from the electricity stations dataset by power station not working time and average power consumptions of clients. The loss of revenue parameter is calculated by multiplying energy losses by energy price per hour. Assuming that new scenario with unticked Use Region of Interest checkbox was created. Unfold the Lifeline category and select the Clients not served with Electricity Analysis option and press Finish. Grand Agreement [Public] Page 40

42 The analysis graph View will be opened. Click on the blue boxes Electricity Substations in order to select an RCP datasets. Press the Search for a dataset option and select an ingested dataset from the popup window. Then press Finish. The blue box will become green as the input dataset has been chosen. Select a name for the output file Select the Station out of work time Select the Price per hour Select the Average power consumption per customer Press the Execute button After some seconds the analysis execution will be completed. A new file will be created in the Scenario tree under the folder name Clients not served with Electricity Analysis. Double click the file to view its contents Grand Agreement [Public] Page 41

43 Highways Operator Loss of Revenue due to Fire Incident It analyses the loss of revenue (unpaid tolls) for highway operator due to highway shutdown as a result of fire incident. For this purpose unpaid tolls due to damaged highway are computed. The number of vehicles that use highway segment per day retrieved from highwaystopology dataset. The duration of highway shutdown, loss of revenue per vehicle per hour have to be entered by the user. The analysis calculates the losses due to highway not working time by multiplying the number of vehicles by highway shutdown time and losses of revenue due to fire incident. Assuming that new scenario with unticked Use Region of Interest checkbox was created. Unfold the Lifeline category and select the Highways Topology Loss of Revenue due to Fire Incident option and press Finish. Grand Agreement [Public] Page 42

44 The analysis graph View will be opened. Click on the blue boxes Highway Information, Fire Contours in order to select an RCP datasets. Press the Search for a dataset option and select an ingested dataset from the popup window. Then press Finish. The blue box will become green as the input dataset has been chosen. Select a name for the output file Select the Duration of Highways Shutdown Select the Fire Safety Distance Select the Loss of Toll Revenue per Vehicle and Highway Segmen Press the Execute button After some seconds the analysis execution will be completed. A new file will be created in the Scenario tree under the folder name Highways Topology Loss of Revenue due to Fire Incident. Double click the file to view its contents Grand Agreement [Public] Page 43

45 Highway Operator Loss of Revenue as cascading effect of electricity blackout It analyses the loss of revenue (unpaid tolls) for highway operator due to highway shutdown as a result of electrical substation equipment damage. For this purpose unpaid tolls due to damaged highway as a result of an electrical substation equipment damage are computed. Electrical substation equipment state is retrieved from highwaystopology dataset. The number of vehicles that use highway segment per day retrieved from highwaystopology dataset. The duration of highway shutdown, loss of revenue per vehicle per hour have to be entered by the user. The current state of highway segment is defined by analysis of nearest substation. If the nearsest substation is damaged then whole highway segment is damaged too. The analysis calculates the losses due to highway not working time by multiplying the number of vehicles by highway shutdown time and costs of revenue per vehicle. Assuming that new scenario with unticked Use Region of Interest checkbox was created. Unfold the Lifeline category and select the Highways Topology Loss of Revenue due to nearest Substation Damage option and press Finish. Grand Agreement [Public] Page 44

46 The analysis graph View will be opened. Click on the blue boxes Highway Segments, Electricity Substations with ID and Damage, Highways Access Points in order to select the datasets. Press the Search for a dataset option and select an ingested dataset from the popup window. Then press Finish. The blue box will become green as the input dataset has been chosen. Select a name for the output file Select the Duration of Highways Shutdown Select the Loss of Toll Revenue per Vehicle and Highway Segment Press the Execute button After some seconds the analysis execution will be completed. A new file will be created in the Scenario tree under the folder name Highways Topology Loss of Revenue due to nearest Substation Damage. Double click the file to view its contents Grand Agreement [Public] Page 45

47 Forest Fire Smoke Operational Damage Analysis In this analysis the following forest fire impacts are considered: 1. Operational damage: If smoke concentration over transmission line (overlay function) exceeds certain threshold then cut power 2. Maintenance actions: Smoke deposition (over entire event) exceeds certain threshold (Threshold is user defined) Assuming that new scenario with unticked Use Region of Interest checkbox was created. Unfold the Decision Support category and select the Forest Fire Smoke Operational Damage Analysis option and press Finish. Grand Agreement [Public] Page 46

48 The analysis graph View will be opened. Click on the blue boxes Power Network Links and Fire Smoke Hazard in order to select the datasets from your local repository. Press the Search for a dataset option and select an ingested dataset from the popup window. Then press Finish. The blue box will become green as the input dataset has been chosen. Select a name for the output file Grand Agreement [Public] Page 47

49 Enter the Ambient Temperature value Enter the smoke concentration threshold (mikrograms/m^3) All graph boxes will become green. Press the Execute button D7.9 Administration and User Manuals for SimICI System final After some seconds the analysis execution will be completed and in the map the lines that need to be powered off are depicted in red color. Flood Inundation Hazard Analysis This analysis is based on the caflood simulator. The caflood application performs a 2D pluvial flood inundation simulation using cellular automata (CA) techniques instead of solving the classic shallow water equations (SWEs). This simplification dramatically reduces the computational load of a CA model in comparison to a physically based model. The caflood application is part of the CADDIES framework/project which is final aim is to produce faster algorithms for handling dual drainage flood modelling, i.e. where the urban surface flow (major system) is combined with the sewer flow (minor system). This is achieved by using CA techniques together with modern parallel hardware. Click the New Analysis option and choose the Flood Inundation Hazard Analysis from the Hazard analysis category Grand Agreement [Public] Page 48

50 Click the Digital Elevation Model box and select the DEM of the Virtual City. If no dataset exists then ingest a new dataset as Raster -> Digital Elevation Model type. The image below shows the loaded DEM of the area of interest and the GIS layers on top (road network, drinking water network) Click the Rainfall Event Dataset box and select the rainfall for your run. If no rainfall exists then ingest a new dataset as Text File -> Rainfall Event Dataset. Click the analysis box and select the parameters of CADDIES: Grand Agreement [Public] Page 49

51 Time Start (seconds) is generally equal to zero, meaning the first time step corresponding to the beginning of the event and it can be used to start a simulation from a certain point in time. In future version of the software, this allows the CADDIES-2D to resume a simulation from a previously produced output. Time End (seconds) is the timing to end of the simulation. Max DT (seconds) sets the upper limit for the time step DT. The DT is defined as t= l/v_max α where l is the length of a cell and v_max is the maximum velocity in the domain (α is introduced later). When velocity is approaching zero, the time step tends to be infinite and thus the computation would stop. Therefore an upper limit of DT has to be defined. The value of Δt is automatically updated by the program every update DT seconds (see later). Generally an upper limit of 60 seconds is recommended by the software developer. Min DT (seconds): the CADDIES-2D uses adaptive time step to capture the movement of flow front. The time step reduces as the maximum velocity increases. In contrast to the Max DT, very small time step may occur when the velocity is high and it will affect the model performance significantly. Therefore, it is mandatory to set a minimum value. Generally a lower limit of 0.01 seconds is recommended by the software developer. Update DT (seconds): Δt changes according to the velocity. Theoretically it should be update at every time step; however to compute Δt at each and every time step is a time consuming task. The user can set up an interval called update DT in which the same Δt t is used. At the end of each interval the new Δt is computed. In this version of the software the value cannot be over 60 and must be an integer factor of 60 such as 10, 20 or 30 Grand Agreement [Public] Page 50

52 seconds. Software developer recommends using 60 seconds to not affect the performance and to provide good accuracy. Slope Tolerance (%): in the WCA2Dv2 model, the time step Δt that the software uses at each update interval is computed using the formula suggested by Hunter et al. (2005): Δt= x ^2/4 min(2n/r^(5 3) S^(1 2) ),S>σ This formula has the drawbacks that when the slope between two cells tends to be zero, the time step is driven to zero. Thus the slope tolerance σ is used to ignore all the cells where the water slope is less than the tollerance. A rule of thumb is to use an order of magnitude less than the average slope percent of the terrain used. For example, if the average slope is 5.28%, then the value of the slope tolerance should be Max Iterations It is necessary to set a number just to be sure that the simulation stops in case Δt becomes very small. Roughness Global: this is the roughness value which is applied to all the cells of the domain. Ignore WD (meter): this parameter is used to speed up the model by ignoring the flow dynamic for cells with very shallow water. During the simulation, if a cell at a given time step has water depth (WD) < m, the model assumes there is NO OUTFLOW for the cell and skips the computing. However, there might always be inflow from the surrounding cells. In other words, in order to have outflow from a given cell the condition WD m must be satisfied. Tolerance (meter): This is another parameter used to speed up the simulation. During the simulation, if two neighbor cells have a water level difference that is less than the tolerance, then the caflood application assumes there is NO OUTFLOW between the pair and skips the computation. Boundary Ele (Hi/Closed-Lo/Open): this parameter sets the boundary condition. If a catchment (or a spatial domain) has very low boundary elements, the flow will be free to leave the domain and the exiting volume is a volume lost. On the contrary, by setting a high value of the boundary cell it is like virtually closing the domain and, at the downstream of the catchment or the exit point of the domain, the water keeps accumulating. For our purposes it is better to set a very low and negative value (e.g., -9000). Output Period (s): the output displayed on the screen is updated once every specified seconds from the start of the simulation (in this case 600s, i.e., once every ten minutes in the simulation time, no run-time). The number of outputs depends on the length of the simulation (Time End). For example a 2 hours simulation and an output period of 600 seconds produce twelve outputs to console. Output Computation Time: true or false. If the parameter is set as true then the computation time is printed on the screen. In the opposite case not. Check Volumes: true or false. Check that the mass conservation is respected or at least preserved within a certain error. Remove Proc Data (No Pre-Proc): true or false. When this command is activated it removes all the temporary CADDIES-2D data files generated during the simulation. Note: the files generated during the preprocessing phase are not involved by this command. Remove Pre-Proc Data: this command complements pt.31. If the parameter is set on true then all files created during preprocessing are also deleted. Raster VEL Vector Field: true or false. When this command is not activated (false) and there is a Raster Grid output-setup-file for the velocity, two output ASCII grid files are generated Grand Agreement [Public] Page 51

53 which show respectively the speed and the angle of the velocity for each cell. If this command is activated (true), only a single output CSV file is generated where each line contains the velocity information of one cell. This information are: the X and Y coordinates of the cell centre, the speed, the angle in radian, and the angle in degree. This command is used to plot the velocity of the cells as vectors in GIS software instead than a raster grid. Raster WD Tolerance (meter): when caflood has run the simulation it takes the input of this instruction to set a threshold for wet and non-wet cells before saving the output. Referring to the example, all cells with less than 0.01 m of water depth will be treated as dry and therefore no water depth will be exported for those cells. All the raster output are influenced by this parameter, i.e. any eventual depth and velocity outputs. The user has to consider that the value of this parameter strongly depends on the vertical accuracy of digital elevation model, the type of terrain, the grid resolution and the quality of the precipitation and infiltration type of data. Upstream Reduction (meter): it is used by Ignore Upstream command to identify the amount of meter to reduce the possible elevation height at each update step. Select the Optional parameters from the respective Tab Use GPU: Check if for GPU acceleration Update Peak Every DT: true or false. When this command is activated, the maximum/peak values of a physical variable are saved at every time step. The default is false; the maximum values are saved only every update DT (usually 60 seconds). Empirical tests showed that this command, when activated, has an insignificant impact on the accuracy but a large impact on the run time of the simulation. Expand Domain: true or false. When this command is not activated (false), the computational domain is the full domain, i.e. each cell with elevation data is processed. When this command is activated, the computational domain is set to be the same of the given events and it will expand when water reach the border of the computational domain. This command is useful to save run time when a simulation has a limited number of point sources of water like an inflow event from a small area. Ignore Upstream: true or false. When this command is activated the model identifies the elevation height where the water is still, i.e. no outflow is generated during an update step. Once a new height is identified all cells with elevation above the height are not considered for computation any more. This can save some run-time depending on the terrain and on Grand Agreement [Public] Page 52

54 the type of events modeled. At the worst case scenario, it might add around 1% of extra run-time. When all input datasets and mandatory parameters have been chosen the blue boxes will become green. Then press the Execute button. Upon completion the flood maximum water depth raster is created and automatically ingested into the local repository (see Figure below). Flood Physical Impact on Drinking Water Assets This analysis calculates the flood impact on drinking water assets according to the HAZUS methodology. As such is uses Damage curves and associated mapping XML files that represent the physical impact per asset characteristics according to the maximum water depth. Click the New Analysis option and choose the Flood Physical Impact on Drinking Water Assets from the Lifelines analysis category Grand Agreement [Public] Page 53

55 Click the Drinking Water Assets, Damage Curves, Damage Curves Asset Mapping and Flood Maximum Flow Depth Hazard boxes and select ingested data types from your repositories. Enter a name for the resulting file and press the Execute button. Upon completion a shapefile is produced based on the input Drinking Water assets shapefile and the dataset is shown on the Map with proper styling depending on the damage percent. Grand Agreement [Public] Page 54

56 Flood Physical Impact on Waste Water Assets This analysis calculates the flood impact on waste water assets according to the HAZUS methodology. As such is uses Damage curves and associated mapping XML files that represent the physical impact per asset characteristics according to the maximum water depth. Click the New Analysis option and choose the Flood Physical Impact on Waste Water Assets from the Lifelines analysis category Grand Agreement [Public] Page 55

57 Click the Waste Water Assets, Damage Curves, Damage Curves Asset Mapping and Flood Maximum Flow Depth Hazard boxes and select ingested data types from your repositories. Enter a name for the resulting file and press the Execute button. Upon completion a shapefile is produced based on the input Waste Water assets shapefile and the dataset is shown on the Map with proper styling depending on the damage percent. Grand Agreement [Public] Page 56

58 3 CADDIES 2D user manual CADDIES 2D flood model has been integrated within CIRP in the EU-CIRCLE project. The model can be executed from CIRP and the results can be displayed within CIRP. It is also possible to run CADDIES as an independent tool and the following is the detailed instruction of using CADDIES. 3.1 Introduction The caflood application performs a 2D pluvial flood inundation simulation using cellular automata (CA) techniques instead of solving the classic shallow water equations (SWEs). The CA technique offers a versatile method for modelling complex physical systems using simple operations (Wolfram 1984). This simplification dramatically reduces the computational load of a CA model in comparison to a physically based model. CA model usually consists of five essential features: a discrete space, the distribution of the neighbour cells, the state of the cells, the discrete time step and the transition rules (Itami 1994). The transition rules are composed of simple operations that govern the evolution of each cell s state. This makes use of the previous state of the cell itself and those in its neighbourhood. Since computing the new state of a cell does not depend on the state of any other cells at the same time step, CA algorithms are well suited to parallel computation. The caflood application is part of the CADDIES framework/project which is final aim is to produce faster algorithms for handling dual drainage flood modelling, i.e. where the urban surface flow (major system) is combined with the sewer flow (minor system). This is achieved by using CA techniques together with modern parallel hardware. Figure 6. Example of interactions between 2D surface flow and 1D sewer flow via linking elements (manholes) The caflood application implements the Weighted Cellular Automata 2D model (WCA2D) with simplified transition rules to simulate inundation events instead of using complex physically based equations and mathematical operations. This model improves the methodology adopted in the CA2D model (Ghimire et al. 2013) using a weight-based system. The WCA2D is a diffusive-like model that ignores inertia terms and momentum conservation. The model has been designed to work with various general grids, (e.g., rectangular, hexagonal or triangular grid) with different neighbourhood types (e.g., the four cells of the von-neumann (VN) neighbourhood or the eight cells of the Moore neighbourhood). The major points of this model are: The ratio of water transferring from the central cell to the downstream neighbour cells (intercellularvolume) is calculated using a minimalistic and quick weight-based system; The volume of water transferring between the central cell and the neighbour cells is limited by a single equation, which is a composition of a simplified Manning s formula and the critical flow condition. Both the adaptive time step and the velocity, as an average velocity, are evaluated within a larger update time step to speed up the simulations. Additional information about the WCA2D model is available in (Guidolin et al. 2015) The caflood application uses the CADDIES Application Programming Interface (API) to implement the 2D model (Guidolin et al. 2012). The CADDIES API defines a standard set of methods, data structures and Grand Agreement [Public] Page 57